@inproceedings{liu-xiong-2026-mass,
    title = "{MASS}: Deep Research for Social Sciences with Memory-Augmented Social Simulation",
    author = "Liu, Yongrui  and
      Xiong, Deyi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.988/",
    pages = "19739--19758",
    ISBN = "979-8-89176-395-1",
    abstract = "Deep Research agents powered by Large Language Models (LLMs) have exhibited extraordinary potential in automated paper writing tasks. However, existing systems rely heavily on literature retrieval and synthesis through internet and local knowledge bases, often resulting research lacking insight and creativity in social science. To address this issue, we propose ``Memory-Augmented Social Simulation (MASS)'', an innovative paradigm that leverages highly realistic and research-oriented social simulations to the creativity and empirical founding of LLMs-generated research. Specifically, MASS integrates three core components{---}dynamic goal-path planning with multi-level social norm restraint to guide the simulation, a multi-disciplinary behavior dataset for agent memory cold-start, and a structured forgetting mechanism inspired by the Ebbinghaus curve. Together, these ensure simulation authenticity and provide a robust empirical foundation for generating innovative scholarly papers. Experimental results demonstrate the effectiveness of our method, showing a 6.81{\%} improvement in generation overall quality over foundation LLMs and 17.19{\%} gain in Insight over strong baselines. Dataset and codes will be released."
}